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Learn about the winners of the Call for Code Global Challenge Round 4, watsonx Prize, and watsonx Knowledge Quiz!

Call for Code is so grateful for the amazing participation from our community of developers and problem solvers throughout the 2023 Call for Code Global Challenge. A big thank you to all who participated this year and shared your solutions to sustainability issues using IBM AI and watsonx. The fourth and final round of this year’s Global Challenge included so many exciting, big ideas to address sustainability with technology. Congratulations to all of our Round 4 winning teams, as well as the winners from our additional watsonx Prize Challenge and watsonx Knowledge Quiz. Read more about how the Round 4 winning teams answered the call.


Winning developer team: India region

Participant(s) location: India

Climate change is causing numerous challenges for farmers, and they have no choice but to adopt more sustainable practices to stay in business. They need to optimize resource usage, adapt to changing weather patterns, maintain and improve access to fair markets, and streamline communications to resolve issues, all of which can be especially challenging for small-scale farmers who often lack the resources available to larger farms. The AGNO team from Hexaware sought to help these farmers get better access to data and recommendations that can help them improve their food yields and deal with uncertainty brought about by climate change. Real-time weather forecasts and AI-driven crop management information are provided in their “FARMISTAR” solution: an online marketplace designed to empower farmers with insights. Several IBM AI services including and Watson Assistant are used to do things like improve the quality of chatbot responses to user queries, provide rain and crop yield predictions, and recommend suitable fertilizers. Future planned enhancements include integrations with IoT devices for real-time monitoring of critical farm conditions, use of IBM Maximo Visual Inspection to help farmers with early detection of crop disease and assessment of harvested crop quality, and additional use of watsonx to improve context-aware and up-to-date educational content for farmers.

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Winning developer team: Canada

Participant(s) location: Canada, U.S.

According to the UN, an estimated 17% of total global food production is wasted in households, food service, and retail. Based on their experiences trying to utilize the produce in their own homes before it spoiled, the FlavourFinder team from Morgan Stanley wanted to create a fun and effective solution to the problem of home food waste. Their mobile app encourages home consumers to make better use of the food they have purchased before it spoils. Users can upload a photo of the food in their refrigerator and, using image recognition, the app then provides recipes based on the available ingredients, encouraging use of more foods that would otherwise end up as trash. Watsonx Assistant manages the interaction to walk someone through using the application so that it’s easy for them to upload their image and get back recipe recommendations and maximize the use of the foods already in their home. The team hopes that in future iterations of the app they can leverage IBM Maximo and substantially expand their training dataset to make predictions more accurate and to identify a wider variety of foods. They would also like to refine their image detection process by identifying the freshness levels of foods to prioritize recipes for food items that are least fresh. They also hope to add tools to gamify the application for users and also allow them to connect with food banks or families in their local community to share excess produce as well.

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Winning university team

University of Sydney

Over 17 million hectares of land in Indonesia are used for coal mining, contaminating adjacent farmland with heavy metals and thereby reducing crop yield by up to 50%. Land rehabilitation is possible by executing phytoremediation, a process which uses certain crops to extract heavy metals from the soil. However, many farmers do not understand which plants are best for their land. A team of students majoring in a diverse mix of subjects such as Banking, Business Analytics, Finance, Marketing, and Food Science at the University of Sydney came together to form team Phyto. Their solution uses geospatial and weather analytics to design a tailored phytoremediation plan for each farmer. Based on a farmer’s location, the app uses IBM Environmental Intelligence Suite to access satellite images, which are then analyzed to determine contamination levels, soil conditions, moisture levels, and nearby vegetation. A Watson machine learning model then uses IBM Weather Data APIs to access these soil conditions and select the most suitable phytoremediation crop for the farmer to plant. For the farmers, analysis is all done remotely, reducing the need for them to do manual soil analysis on site. Additionally, once the plants are in place, the app includes growing guidelines, weather alerts, and land rehabilitation resources to help ensure the success of the new crop. Users can also use the “connect” feature to engage with community members and experts. Lastly, the “sell” feature provides access to an online marketplace which connects farmers to small local businesses who are sourcing raw materials. With little to no coding experience, the Phyto team was able to learn Watson Studio quickly to build their solution. They hope their solution can also eventually be used for larger scale land rehabilitation projects.

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Winning developer team: Central and Eastern Europe

Participant(s) location: Finland, France, U.S.

In addition to their 4th Round prize, the RAG&AI team also took home the special watsonx Prize, worth $2,500 USD, for the best use of IBM technology.

The fashion industry’s current practices of producing “fast fashion” and excessive clothing have led to a large textile waste problem. According to the UN, the apparel industry consumes more energy than the aviation and shipping industries combined and produces 8-10 percent of the world’s greenhouse gas emissions and 20 percent of waste water. Additionally, $500 billion is lost annually due to clothing underutilization. While thrift stores can play a crucial role in reselling excess items, they often struggle to effectively catalog and offer all the clothing that gets donated. The RAG&AI solution allows stores to create a digital inventory by photographing garments. Clothing photographs are run through image recognition and tagged with keywords. Then and large language models from IBM are used to clean key word tags assigned to each image to be more relevant, so that inventory is more easily searchable and sortable. Consumers can interact with clothing for sale to find items they are looking for at local shops, promoting reuse. Even team members who had not done any data engineering before found it easy to get started with the Prompt Lab and integrate generative AI capabilities into the RAG&AI app. The team hopes to expand a future version of their solution, using to improve garment descriptions and make the buying process even easier for consumers.

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Synergy Squad

Winning ISV team

Participant(s) location: India, Malaysia

Approximately 1 out of every 3 people in the world does not have access to adequate food. Food waste also contributes to greenhouse gas emissions. The Synergy Squad team from Persistent is hoping their solution can address food insecurity and reduce food waste by making sure less food purchased by consumers goes to waste. Their “Offshelf” solution enables users to track the food in their homes, receive expiry notifications, and share surplus item within the community to reduce the chances of good food going bad. AI recognizes fresh items from a photo or voice description and provides an estimated expiration date, or packaged items can be scanned to input the date. Push notifications would let users know when groceries are about to expire, reducing the likelihood of wasted food. When users cannot make use of their groceries, the solution also includes a platform to facilitate the sharing of surplus food within the local community. Watson Speech to Text is used to help users fill out forms in the application through voice input. The team hopes to add to Offshelf, with additional image recognition support to read expiration dates and other details from food labels faster.

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watsonx Knowledge Quiz

On October 13, the watsonx Knowledge Quiz took place as part of Round 4 of the Call for Code Global Challenge. Participants took part in an online quiz to test their knowledge on IBM watsonx. The 25 top scoring participants won $25 USD each. Participants from all over the globe took the quiz, and congratulations are in order to these well-deserving Call for Code winners:

Faruq Abidoun, Swastik Aggarwal, Pranjal Agrawal, Maulik Avantikumar, Pritam Ashok Dahiphale, Akinola Daramola, Vijay Dessa, Sanket Dilip, Ayush Dongardive, Crystal Dsouza, Salma Gambo, Arpit Singh Gautam, Riya Jangam, Harsha Kaslikar, Elvis Kiilu, Karan Kumar, Suhas Kumar, Mark Muta, Jean-Georges Perrin, Om Prakash, Kaustubh Sathawane, Talat Shaheen, Parag Tonpe, Vijayakumar Venugopal, Will Worrell

A digital credential from the IBM Digital Badge program was also issued to all participants who submitted a solution during the 2023 Call for Code Global Challenge that demonstrated use of one or more IBM AI services.

Grand prizes in December

In early December, Call for Code will announce the grand prize winners for the entire 2023 Global Challenge. Winners from all four rounds of this year’s challenge will have their projects evaluated by a panel of eminent judges to determine which teams will take home the top awards. Grand prize winners will receive $50,000 USD, solution implementation support from the Call for Code ecosystem, and more. Stay tuned for the exciting announcement of winners! In the meantime, explore the Call for Code web site for more ways you or your organization can get involved and answer the call.

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